As a blogger almost as obsessive-compulsive about literature and philosophy as I am about data, this post resonated with me. But perhaps Neil Raden is right when he remarked on Twitter that “anyone who works in Jean-Paul Sartre with data migration should get to spend 90 days with Lindsay Lohan. Curse of liberal arts education.” (Please Note: Lindsay’s in jail for 90 days).

Part of my liberal arts education (and for awhile I was a literature major with a minor in philosophy) included reading Sartre, not only his existentialist philosophy, but also his literature, including the play No Exit, which is the source of perhaps his most famous quote: “l’enfer, c’est les autres” (“Hell is other people”) that I have paraphrased into the title of this blog post.

Being and Nothingness

John Morris used Jean-Paul Sartre’s classic existentialist essay Being and Nothingness, and more specifically, two of its concepts, namely that objects are “en-soi” (“things in themselves”) and people are “pour-soi” (“things for themselves”), to examine the complex relationship that is formed during data analysis between the data (an object) and its analyst (a person).

During data analysis, the analyst is attempting to discover the meaning of data, which is determined by discovering its essential business use. However, in the vast majority of cases, data has multiple business uses.

This is why, as Morris explains, first of all, we should beware “the naive simplicity of assuming that understanding meaning is easy, that there is one right definition. The relationship between objects and their essential meanings is far more problematic.”

Therefore, you need not worry, for as Morris points out, “it’s not because you are no good at your job and should seek another trade that you can’t resolve the contradictions. It’s a problem that has confused some of the greatest minds in history.”

“Secondly,” as Morris continues, we have to acknowledge that “we have the technology we have. By and large, it limits itself to a single meaning, a single Canonical Model. What we have to do is get from the messy first problem to the simpler compromise of the second view. There’s no point hiding away from this as an essential part of our activity.”

The complexity of the external world

“Machines are en-soi objects that create en-soi objects,” Morris explains, whereas “people are pour-soi consciousnesses that create meanings and instantiate them in the records they leave behind in the legacy data stores we then have to re-interpret.”

“We then waste time using the wrong tools (e.g., trying to impose an enterprise view onto our business domain experts which is inconsistent with their divergent understandings) only to be surprised and frustrated when our definitions are rejected.”

As I have written about in previous posts, whether it’s an abstract description of real-world entities (i.e., “master data”) or an abstract description of real-world interactions (i.e., “transaction data”) among entities, data isan abstract description of reality.

These abstract descriptions can never be perfected since there is always what I call a digital distance between data and reality.

The inconvenient truth is that reality is not the same thing as the beautifully maintained digital data worlds that exist within our enterprise systems (and, of course, creating and maintaining these abstract descriptions of reality is no easy task).

As Morris thoughtfully concludes, we must acknowledge that “this central problem of the complexity of the external world is against the necessary simplicity of our computer world.”

Hell is other people’s data

The inconvenient truth of the complexity of the external world plays a significant role within the existentialist philosophy of an organization’s data silos, which are also the bane of successful enterprise information management.

Each and every business unit acts as a pour-soi (a thing for themselves), persisting on their reliance on their own data silos, thereby maintaining their own version of the truth—because they truly believe that hell is other people’s data.